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Power and Effect Size Cal State Northridge  320 Andrew Ainsworth PhD.

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Presentation on theme: "Power and Effect Size Cal State Northridge  320 Andrew Ainsworth PhD."— Presentation transcript:

1 Power and Effect Size Cal State Northridge  320 Andrew Ainsworth PhD

2 2 Major Points Review What is power? What controls power? Effect size Power for one sample t Power for related-samples t Power for two sample t Psy Cal State Northridge

3 3 Important Concepts Concepts critical to hypothesis testing –Decision –Type I error –Type II error –Critical values –One- and two-tailed tests Psy Cal State Northridge

4 4 Decisions When we test a hypothesis we draw a conclusion; either correct or incorrect. –Type I error Reject the null hypothesis when it is actually correct. –Type II error Retain the null hypothesis when it is actually false. Psy Cal State Northridge

5 5 Type I Errors Null Hypothesis really is true We conclude the null is false. This is a Type I error –Probability set at alpha (  )  usually at.05 –Therefore, probability of Type I error =.05 Psy Cal State Northridge

6 6 Type II Errors The Alternative Hypothesis is true We conclude that the null is true This is also an error (Type II) –Probability denoted beta (  ) We can’t set beta easily. We’ll talk about this issue later. Power = (1 -  ) = probability of correctly rejecting false null hypothesis. Psy Cal State Northridge

7 7 Confusion Matrix Psy Cal State Northridge

8 8 Critical Values These represent the point at which we decide to reject null hypothesis. e.g. We might decide to reject null when (p|null) <.05. –In the null distribution there is some value with p =.05 –We reject when we exceed that value. –That value is called the critical value. Psy Cal State Northridge

9 9 One- and Two-Tailed Tests Two-tailed test rejects null when obtained value too extreme in either direction –Decide on this before collecting data. One-tailed test rejects null if obtained value is too low (or too high) –We only set aside one direction for rejection. Psy Cal State Northridge

10 10 One- & Two-Tailed Example One-tailed test –Reject null if IQPLUS group shows an increase in IQ Probably wouldn’t expect a reduction and therefore no point guarding against it. Two-tailed test –Reject null if IQPLUS group has a mean that is substantially higher or lower. Psy Cal State Northridge

11 11 What Is Power? Probability of rejecting a false H 0 Probability that you’ll find difference that’s really there 1 - , where  = probability of Type II error Psy Cal State Northridge

12 12 What Controls Power? The significance level (  ) True difference between null and alternative hypotheses  1 -  2 Sample size Population variance The particular test being used Psy Cal State Northridge

13 13 Distributions Under  1 and  0 Psy Cal State Northridge

14 14 Effect Size The degree to which the null is false –Depends on distance between    and   –Also depends on standard error (of mean or of difference between means) Psy Cal State Northridge

15 15 What happened to n? It doesn’t relate to how different the two population means are. It controls power, but not effect size. We will add it in later. Psy Cal State Northridge

16 16 Estimating Effect Size Judge your effect size by: –Past research –What you consider important –Cohen’s conventions Psy Cal State Northridge

17 17 Combining Effect Size and n We put them together and then evaluate power from the result. General formula for Delta –where f (n) is some function of n that will depend on the type of design Psy Cal State Northridge

18 18 Power for One-Sample or Related samples t First calculate delta with: –where n = size of sample, and  and  as above Look power up in table using  and significance level (  ) Psy Cal State Northridge

19 19 Power for Single Sample IQPLUS Study One sample z and t –Compared IQPLUS group with population mean = 100, sigma = 10 –Used 25 subjects –We got a sample mean of 106 and s = 7.78 Psy Cal State Northridge

20 20 IQPLUS Assuming we don’t know sigma –  = 0.77 – n = 25 – –We are testing at  =.05 –Use Appendix D.5 Psy Cal State Northridge

21 21 Appendix D.5 This table is severely abbreviated. Power for  = 3.85,  =.05 Psy Cal State Northridge

22 22 Conclusions If we can trust our estimates in the IQPLUS study then if this study were run repeatedly, 97% of the time the result would be significant. Psy Cal State Northridge

23 23 How Many Subjects Do I Really Need (Single/Related Sample(s))? Run calculations backward –Start with anticipated effect size (  ) –Determine  required for power =.80. Why.80? –Calculate n What if we wanted to rerun the IQPLUS study, and wanted power =.80? Psy Cal State Northridge

24 24 Calculating n We estimated =.77 Complete Appendix D.5 shows we need  = 2.80 Calculations on next slide Psy Cal State Northridge

25 25 IQPLUS n Psy Cal State Northridge

26 26 Power for Two Independent Groups What changes from preceding? –Effect size deals with two sample means –Take into account both values of n Effect size Psy Cal State Northridge

27 27 Estimating d We could calculate d  directly if we knew populations. We could estimate from previous data. Here we will calculate using Violent Video Games example Psy Cal State Northridge

28 28 Example: Violent Videos Games Two independent randomly selected/assigned groups –GTA (violent: 8 subjects) VS. NBA 2K7 (non- violent: 10 subjects) –We want to compare mean number of aggressive behaviors following game play –GTA had a mean of and s = –NBA 2K7 had a mean of 8.4 and s = –s 2 pooled = 2.745, so s pooled = Psy Cal State Northridge

29 29 Two Independent Groups Then calculate  from effect size Note: The above formula assumes that the 2 groups have equal n Psy Cal State Northridge

30 30 Two Independent Groups Our data do not have equal n, but let’s pretend they do for a moment (both 10) For our data Psy Cal State Northridge

31 31 Appendix D.5 This table is severely abbreviated. Power for  = 2.5,  =.05 Estimate =.71 Psy Cal State Northridge

32 32 Conclusions If we had equal n and we can trust our estimates in the violent video game study then if this study were run repeatedly, 71% of the time the result would be significant. Psy Cal State Northridge

33 33 Unequal Sample Sizes With unequal samples use harmonic mean of sample sizes Where k is the number of groups (i.e. 2), n i is each group size Standard arithmetic average will work well if n ’s are close. Psy Cal State Northridge

34 34 Two Independent Groups Our data do not have equal n, so we need to find the harmonic mean For our data Psy Cal State Northridge

35 35 Two Independent Groups Our data do not have equal n, so… For our data Psy Cal State Northridge

36 36 Appendix D.5 This table is severely abbreviated. Psy Cal State Northridge Power for  = 2.4,  =.05 Estimate =.67

37 37 Conclusions If we we can trust our estimates in the violent video game study then if this study were run repeatedly, 67% of the time the result would be significant. Psy Cal State Northridge

38 38 How Many Subjects Do I Really Need (Independent Samples)? Run calculations backward –Start with anticipated effect size (  ) –Determine  required for power =.80. Why.80? –Calculate n What if we wanted to rerun the violent video game study, and wanted power =.80? Psy Cal State Northridge

39 39 Calculating n We estimated  = Complete Appendix E.5 shows we need  = 2.80 Calculations on next slide Psy Cal State Northridge

40 40 Violent Video Games n Psy Cal State Northridge


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